Data on the geographic distribution of COVID-19 cases worldwide

Libraries

In [1]:
# install calmap
! pip install calmap

Requirement already satisfied: calmap in c:\users\admin\anaconda3\lib\site-packages (0.0.7)
Requirement already satisfied: pandas in c:\users\admin\anaconda3\lib\site-packages (from calmap) (0.24.2)
Requirement already satisfied: numpy in c:\users\admin\anaconda3\lib\site-packages (from calmap) (1.16.4)
Requirement already satisfied: matplotlib in c:\users\admin\anaconda3\lib\site-packages (from calmap) (3.1.0)
Requirement already satisfied: python-dateutil>=2.5.0 in c:\users\admin\anaconda3\lib\site-packages (from pandas->calmap) (2.8.0)
Requirement already satisfied: pytz>=2011k in c:\users\admin\anaconda3\lib\site-packages (from pandas->calmap) (2019.1)
Requirement already satisfied: cycler>=0.10 in c:\users\admin\anaconda3\lib\site-packages (from matplotlib->calmap) (0.10.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\admin\anaconda3\lib\site-packages (from matplotlib->calmap) (1.1.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\users\admin\anaconda3\lib\site-packages (from matplotlib->calmap) (2.4.0)
Requirement already satisfied: six>=1.5 in c:\users\admin\anaconda3\lib\site-packages (from python-dateutil>=2.5.0->pandas->calmap) (1.12.0)
Requirement already satisfied: setuptools in c:\users\admin\anaconda3\lib\site-packages (from kiwisolver>=1.0.1->matplotlib->calmap) (41.0.1)
In [2]:
# essential libraries
import json
import random
from urllib.request import urlopen


import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# storing and anaysis
import numpy as np
import pandas as pd

# visualization
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go
import plotly.figure_factory as ff
import calmap
#import folium

# color pallette
cnf = '#393e46' # confirmed - grey
dth = '#ff2e63' # death - red
rec = '#21bf73' # recovered - cyan
act = '#fe9801' # active case - yellow

# converter
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()   

# hide warnings
import warnings
warnings.filterwarnings('ignore')

# html embedding
from IPython.display import Javascript
from IPython.core.display import display
from IPython.core.display import HTML
In [3]:
dfCovid = pd.read_csv('COVID-19-geographic-disbtribution-worldwide-2020-03-21.csv')
dfCovid.head(3)
Out[3]:
Date Day Month Year Cases Deaths Countries and territories GeoId
0 3/21/2020 21 3 2020 2 0 Afghanistan AF
1 3/20/2020 20 3 2020 0 0 Afghanistan AF
2 3/19/2020 19 3 2020 0 0 Afghanistan AF

Prepocessing : Cleaning Data

In [4]:
# cases 
cases = ['Cases', 'Deaths', 'Active']

# Active Case = confirmed - deaths - recovered
dfCovid['Active'] = dfCovid['Cases'] - dfCovid['Deaths']

# replacing Antigua_and_Barbuda with just Barbuda
dfCovid['Countries and territories'] = dfCovid['Countries and territories'].replace('Antigua_and_Barbuda', 'Barbuda')

# replacing Central_African_Republic with just CAR
dfCovid['Countries and territories'] = dfCovid['Countries and territories'].replace('Central_African_Republic', 'CAR')

# replacing Democratic_Republic_of_the_Congo with just DRC
dfCovid['Countries and territories'] = dfCovid['Countries and territories'].replace('Democratic_Republic_of_the_Congo', 'DRC')

# replacing United_Republic_of_Tanzania with just BarbTanzania
dfCovid['Countries and territories'] = dfCovid['Countries and territories'].replace('United_Republic_of_Tanzania', 'Tanzania')

# replacing United_States_of_America with just Barbuda
dfCovid['Countries and territories'] = dfCovid['Countries and territories'].replace('United_States_of_America', 'USA')

# replacing Cases_on_an_international_conveyance_Japan with just Japan
dfCovid['Countries and territories'] = dfCovid['Countries and territories'].replace('Cases_on_an_international_conveyance_Japan', 'Japan')

# replacing Saint_Vincent_and_the_Grenadines with just Grenadines
dfCovid['Countries and territories'] = dfCovid['Countries and territories'].replace('Saint_Vincent_and_the_Grenadines', 'Grenadines')

# replacing Bosnia_and_Herzegovina with just Bosnia
dfCovid['Countries and territories'] = dfCovid['Countries and territories'].replace('Bosnia_and_Herzegovina', 'Bosnia')




# filling missing values 
dfCovid[['Countries and territories']] = dfCovid[['Countries and territories']].fillna('')
dfCovid[cases] = dfCovid[cases].fillna(0)
In [5]:
#Group by countries
temp = dfCovid.groupby(['Countries and territories'])['Cases', 'Deaths', 'Active'].sum().reset_index()
temp.style.background_gradient(cmap='Pastel1')
temp.head()
Out[5]:
Countries and territories Cases Deaths Active
0 Afghanistan 24 0 24
1 Albania 70 2 68
2 Algeria 94 10 84
3 Andorra 75 0 75
4 Argentina 158 3 155
In [6]:
#Group by Year and Month
temp = dfCovid.groupby(['Year','Month'])['Cases', 'Deaths', 'Active'].sum().reset_index()
temp.style.background_gradient(cmap='Pastel1')
Out[6]:
Year Month Cases Deaths Active
0 2019 12 27 0 27
1 2020 1 9799 213 9586
2 2020 2 75377 2708 72669
3 2020 3 186161 8331 177830
In [7]:
#Group by Year
temp = dfCovid.groupby('Year')['Cases', 'Deaths', 'Active'].sum().reset_index()
temp.style.background_gradient(cmap='Pastel1')
Out[7]:
Year Cases Deaths Active
0 2019 27 0 27
1 2020 271337 11252 260085
In [8]:
sns.distplot(temp['Cases'])
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x203caad3048>
In [9]:
temp = dfCovid.groupby(['Countries and territories', 'Date'])['Cases', 'Deaths'].sum()
temp = temp.reset_index()

fig = px.bar(temp, x="Date", y="Cases", color='Countries and territories', orientation='v', height=600,
             title='Confirmed', color_discrete_sequence = px.colors.cyclical.mygbm)
fig.show()
In [10]:
fig = px.treemap(dfCovid.sort_values(by='Cases', ascending=False).reset_index(drop=True), 
                 path=["Countries and territories"], values="Cases", height=700,
                 title='Number of Confirmed Cases',
                 color_discrete_sequence = px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig.show()

fig = px.treemap(dfCovid.sort_values(by='Deaths', ascending=False).reset_index(drop=True), 
                 path=["Countries and territories"], values="Deaths", height=700,
                 title='Number of Deaths reported',
                 color_discrete_sequence = px.colors.qualitative.Prism)
fig.data[0].textinfo = 'label+text+value'
fig.show()
In [11]:
temp = dfCovid.groupby('Countries and territories')['Cases', 'Deaths', 'Active'].sum().reset_index()
temp.style.background_gradient(cmap='Pastel1')
temp.head(3)
Out[11]:
Countries and territories Cases Deaths Active
0 Afghanistan 24 0 24
1 Albania 70 2 68
2 Algeria 94 10 84
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In [12]:
tm = temp.melt(id_vars="Date", value_vars=['Active', 'Deaths'])
fig = px.treemap(tm, path=["variable"], values="value", height=400, width=600)
fig.show()

Country wise Data : In each country

In [13]:
dfCovid2 = dfCovid.drop(['Day','Month', 'Year'],axis='columns')
dfCovid2.head(3)
Out[13]:
Date Cases Deaths Countries and territories GeoId Active
0 3/21/2020 2 0 Afghanistan AF 2
1 3/20/2020 0 0 Afghanistan AF 0
2 3/19/2020 0 0 Afghanistan AF 0

Countries with deaths reported

In [14]:
df_country = pd.read_csv('Country.csv', encoding='latin1')
df_country.head(5)
Out[14]:
GeoId latitude longitude country_name
0 AD 42.546245 1.601554 Andorra
1 AE 23.424076 53.847818 United Arab Emirates
2 AF 33.939110 67.709953 Afghanistan
3 AG 17.060816 -61.796428 Antigua and Barbuda
4 AI 18.220554 -63.068615 Anguilla
In [15]:
#result = df_country.join(dfCovid2, how='inner')
result = pd.merge(df_country, dfCovid2, on='GeoId');
result.head()
Out[15]:
GeoId latitude longitude country_name Date Cases Deaths Countries and territories Active
0 AD 42.546245 1.601554 Andorra 3/21/2020 0 0 Andorra 0
1 AD 42.546245 1.601554 Andorra 3/20/2020 22 0 Andorra 22
2 AD 42.546245 1.601554 Andorra 3/19/2020 39 0 Andorra 39
3 AD 42.546245 1.601554 Andorra 3/18/2020 0 0 Andorra 0
4 AD 42.546245 1.601554 Andorra 3/17/2020 9 0 Andorra 9
In [16]:
country = result.groupby(['Countries and territories', 'latitude', 'longitude'])['Cases', 'Deaths', 'Active'].sum().reset_index()
country.style.background_gradient(cmap='Pastel1')
country.head()
Out[16]:
Countries and territories latitude longitude Cases Deaths Active
0 Afghanistan 33.939110 67.709953 24 0 24
1 Albania 41.153332 20.168331 70 2 68
2 Algeria 28.033886 1.659626 94 10 84
3 Andorra 42.546245 1.601554 75 0 75
4 Argentina -38.416097 -63.616672 158 3 155
In [17]:
country = result
country.head(5)
Out[17]:
GeoId latitude longitude country_name Date Cases Deaths Countries and territories Active
0 AD 42.546245 1.601554 Andorra 3/21/2020 0 0 Andorra 0
1 AD 42.546245 1.601554 Andorra 3/20/2020 22 0 Andorra 22
2 AD 42.546245 1.601554 Andorra 3/19/2020 39 0 Andorra 39
3 AD 42.546245 1.601554 Andorra 3/18/2020 0 0 Andorra 0
4 AD 42.546245 1.601554 Andorra 3/17/2020 9 0 Andorra 9
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In [18]:
# World wide
#import folium
#from folium.plugins import HeatMap

#!conda install -c conda-forge folium=0.5.0 --yes
import folium
import webbrowser




m = folium.Map(location=[0, 0], tiles='cartodbpositron',
               min_zoom=1, max_zoom=4, zoom_start=1)

for i in range(0, len(country)):
    folium.Circle(
        location=[country.iloc[i]['latitude'], country.iloc[i]['longitude']],
        color='crimson',
        tooltip =   '<li><bold>Countries and territories : '+str(country.iloc[i]['Countries and territories'])+
                    '<li><bold>Cases : '+str(country.iloc[i]['Cases'])+
                    '<li><bold>Deaths : '+str(country.iloc[i]['Deaths']),
            radius=int(country.iloc[i]['Cases'])**1.1).add_to(m)
m
Out[18]:

Countries with Confirmed Cases

In [19]:
# Confirmed

fig = px.choropleth(country, locations="Countries and territories", 
                    locationmode='country names', color="Cases", 
                    hover_name="Countries and territories", range_color=[1,7000], 
                    color_continuous_scale="aggrnyl", 
                    title='Countries with Confirmed Cases')
fig.update(layout_coloraxis_showscale=False)
fig.show()

Countries with Deaths Reported

In [20]:
# Deaths

fig = px.choropleth(country[country['Deaths']>0], 
                    locations="Countries and territories", locationmode='country names',
                    color="Deaths", hover_name="Countries and territories", 
                    range_color=[1,50], color_continuous_scale="agsunset",
                    title='Countries with Deaths Reported')
fig.update(layout_coloraxis_showscale=False)
fig.show()

Spread over time

In [21]:
formated_gdf = country.groupby(['Date', 'Countries and territories'])['Cases', 'Deaths'].max()
formated_gdf = formated_gdf.reset_index()
formated_gdf['Date'] = pd.to_datetime(formated_gdf['Date'])
formated_gdf['Date'] = formated_gdf['Date'].dt.strftime('%m/%d/%Y')
formated_gdf['size'] = formated_gdf['Cases'].pow(0.3)

fig = px.scatter_geo(formated_gdf, locations="Countries and territories", locationmode='country names', 
                     color="Cases", size='size', hover_name="Countries and territories", 
                     range_color= [0, max(formated_gdf['Cases'])+2], 
                     projection="natural earth", animation_frame="Date", 
                     title='Spread over time')
fig.update(layout_coloraxis_showscale=False)
fig.show()

Country Wise

In [22]:
temp = country.groupby(['Date', 'Countries and territories'])['Cases'].sum()
temp = temp.reset_index().sort_values(by=['Date', 'Countries and territories'])

plt.style.use('seaborn')
g = sns.FacetGrid(temp, col="Countries and territories", hue="Countries and territories", 
                  sharey=False, col_wrap=5)
g = g.map(plt.plot, "Date", "Cases")
g.set_xticklabels(rotation=90)
plt.show()
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